Depth Of Cut Research Articles

Influence of Reverse Finishing on Characteristics of Drilling Surface

Aluminum alloys are often used in automobile and aircraft parts that require higher dimensional accuracy and durability. Drilling is often used in the machining process of these products and accounts for about 60% of the machining performed on these products. Deterioration of finishing surface accuracy in drilling causes problems such as decreased tool life due to wear and increased cost due to the introduction of the finishing process. The objective of this study is to evaluate the finished surface characteristics in the drilling of aluminum alloys using reverse finishing, with a focus on the cutting direction of finishing, with respect to the roughing direction. The torque and thrust force are smaller in reverse finishing than in forward finishing. The reduction effects of cutting force in reverse finishing were more significant when the finishing depth of cut was smaller in relation to the roughing-affected layer. Under conditions where the finishing depth was equal to or greater than the roughing-affected layer, it was possible to reduce cutting forces and improve surface roughness while obtaining compressive residual stresses equivalent to forward finishing.

Multi-Criteria Optimization of Al2O3-ZrO2(Y2O3)-based Self-lubricating Composite Cutting Tools for Increasing Shear Angle, and Wear Resistance in Machining Processes

Abstract In the present investigation, self-lubricating cutting tools were fabricated by adding various elements such as NiCr, Ag, Mo, SrSO4, and CaF2 to Zirconia Toughened Alumina in order to overcome the difficulties that occurred in dry machining. Three significant process parameters; cutting speed (50, 125, 200, and 275 m/min), feed rate (0.1 and 0.2 mm/rev), and depth of cut (0.1 and 0.2 mm) were selected for the machining process. The performance measures such as chip thickness ratio, shear angle, and tool wear, were investigated. The experimental runs were conducted with Taguchi L8 mixed-level orthogonal array. Technique for Order of Preference by Similarity to Ideal Solution is a multi-criteria decision-making method used to determine the best alternative from a set of options. Turning experiments have been performed on the AISI 4340 steel workpiece with various combinations of machining parameters. The findings revealed the incorporation of SrSO4 and Mo at 10Wt.% and CaF2 at 5Wt.% (i.e., self-lubricating cutting tool-4) achieved better mechanical properties and wear resistance due to the formation of a self-lubrication layer on the rake face of the cutting tool. A confirmation experiment is executed in order to verify the outcomes in the end. Finally, there is a noticeable improvement in the outcomes compared to the base cutting tool, i.e., chip thickness ratio is 35.77%, shear angle is 27.31%, and tool wear is 58%. The mechanism for improving performance measures is discussed in detail.

Development of smart manufacturing framework for micromilling of thin-walled Ti6Al4V

Identification of chatter is mostly carried out offline after capturing the machining signals or by analyzing the machined surface after completing or interrupting the machining process. The offline methods for analyzing the machined signals are tedious and not very useful for microcomponents attributed to permanent deformation of thin-wall if chatter has already been occurred during process. Real-time information is necessary to monitor and control the micromilling process. Consequently, a smart manufacturing framework has been developed in the present work to monitor the chatter onset and the deviation in geometrical shape of the thin-wall during micromilling. An interactive web interface has been developed to project the real-time information necessary to monitor the micromilling process. The excitation of natural modes of the thin-walled structure of Ti6Al4V has been observed in real-time on the web browser for micromilling at 80 µm radial depth of cut, 50,000 rpm and a feed rate of 3 µm/flute. The maximum permanent deformation of 97.43 µm for thin-wall has been obtained at 130 µm radial depth of cut. The developed layout of web browser can be used for real-time monitoring of any other machining process as well.

Effects of machining parameters, ultrasonic vibrations and cooling conditions on cutting forces and tool wear in meso scale ultrasonic vibrations assisted end-milling (UVAEM) of Ti–6Al–4V under dry, flooded, MQL and cryogenic environments – A statistical analysis

Ti–6Al–4V, an alloy of titanium has recently gained focus of research to overcome its machinability challenges. Miniaturization in devices has forced a shift towards micro and meso-scale machining. Sustainability and green manufacturing concepts have shifted focus in machining from cutting fluids towards modern cooling approaches to minimize or eliminate them. In present research, cutting forces & tool wear in Ultrasonic Vibrations Assisted End Milling of Ti–6Al–4V at meso-scale under Dry, Flooded, Minimum Quantity Lubrication & Cryogenic cooling have been analyzed to develop optimum machining parameters. Taguchi L16 orthogonal array was designed to perform experiments keeping Cutting Speed, Feed per tooth, Depth of cut, Ultrasonic vibrations amplitude & Cooling environment as inputs. ANOVA was used to analyze contribution ratios by these parameters towards cutting forces & tool wear. Results indicated that machining conditions, cooling environment and Ultrasonic Vibrations influence cutting forces & tool wear. Depth of cut had the highest influence with 63.41% contribution towards cutting forces while cutting speed remained the highest influencing factor with 39.86% contribution towards tool wear. Cutting forces were reduced by 33.49%, 16.93% and 4.91% while tool wear was reduced by 26.43%, 9.48% and 5.17% under Minimum Quantity Lubrication environment compared to dry, flooded and cryogenic cooling respectively. Cutting forces and tool wear under ultrasonic vibrations were reduced by 24.52% and 13.16% respectively as compared to Conventional Machining (CM). For optimum results, Minimum Quantity Lubrication with low Cutting Speed, Depth of cut, Feed per tooth and high amplitude of ultrasonic vibrations is recommended to machine Ti–6Al–4V.

Taguchi Design of Experiments to Optimize Material Removal Rate in Turning AISI 4340 Steel

In this work, an effort is prepared to optimize turning process by maximising the material removal rate (MRR). For experimentation, CNC Spinner 15 lathe machine is used. The input factors are spindle speed, feed rate, depth of cut and type of tool under the 3 altered values of noise factor. Output parameter is MRR. The work piece material is AISI4340 steel. The Taguchi robust design is achieved by implementing spindle vibration as a noise factor. The orthogonal Array L9 is used for experimentation. The MRR is calculated by the defined formula. The data analysis is done by Taguchi technique. The Higher-the-better signal-noise ratio is adopted to compute the quality characteristics S by N fraction of MRR for every run of experiment. Response table is then computed. The main effect plot is the base to discover the best situation of the response parameter. ANOVA is used to guess the significant parameters. The best setting is A-1 B-2 C-2 D-3 and E-3) i.e. spindle speed 780rpm, feed rate = 2.42 mm/rev, coated CVD tool, nose radius=1.2 mm whereas depth of cut = 1.5 mm.

Acoustic Signal-Based Robotic Cutting Depth Control Under Uncertain Bone Layer Distribution

Acoustic Signal-Based Robotic Cutting Depth Control Under Uncertain Bone Layer Distribution

Indirect measurement of process parameters in peripheral end milling based on acoustic emission signals

In machining, current methods for online estimation of cutting parameters heavily rely on piezoelectric dynamometers. Albeit their high precision, integration into industrial environments is limited due to high cost and practical machine mounting constraints. To address this challenge, this study presents an indirect measurement method of the cutting depths in peripheral milling based on acoustic emission sensors. The proposed method involves extracting time-based features from a tailor-made envelope of the acoustic emission signal, identifying transitions between cutting phases from its waveform, and using these features to estimate radial and axial depths of cut through a geometric model of the tool-workpiece contact zone. Experiments show that this method can achieve relative errors below 5% in measuring the depths of cut, approaching the precision levels of piezoelectric dynamometers. This suggests that acoustic emission is an alternative process variable, enriching the spectrum of monitoring systems traditionally dependent on the measurement of cutting forces.

Orthogonal array and artificial neural network approach for sustainable cutting optimization machining of 17-4 PH steel under CNC wet turning operations

Sustainable manufacturing strives to increase output while minimizing resource usage, costs, and environmental impact. Tool longevity is crucial, considering material, power, and resource consumption. Challenges like chip removal, heat, and friction necessitate effective solutions. Cutting fluids, like rice bran oil, reduce temperatures, friction, and enhance tool life and surface quality. Research focuses on using rice bran oil with a TiAlN tipped tool to minimize friction and cutting forces. Taguchi's robust design and L-9 orthogonal array reduce experimental trials. Controllable factors include speed, feed, depth of cut, and rice bran oil flow rate, with surface roughness as the performance metric. Taguchi analysis improves turning performance, particularly with 17-4 PH stainless steel. Rice bran oil reduces frictional forces, enabling improved surface quality across all samples, promoting sustainability. Surface roughness (Ra) is notably influenced by feed rate and rice bran oil, contributing 58.06% and 49.5%, respectively. This research underscores the potential of optimizing cutting fluid and process parameters to enhance sustainability in manufacturing.

The effect of cutting depth, feed rate, and cutting angle on surface roughness in the lathe process of aluminum 6061.

In the turning process, the machining parameters have a significant impact on the product quality. Consumers frequently request that certain industries adhere to product quality standards, especially regarding surface texture. Numerous product failures have resulted in dissatisfaction among the company's clientele because many lathe operators are fixated solely on product size drawings and are unaware of factors that can impact the surface roughness value. This investigation was conducted to ascertain how machining parameters affect surface irregularity. Variations in feed rates of 47.5 mm/min and 345.6 mm/min, cutting angles of 60° and 80°, and depths of cut of 1 mm and 3 mm were utilized to conduct the investigation on aluminum alloy 6061 specimens. The results of turning were evaluated for surface roughness using a surface roughness analyzer and a macro test to determine the structure of the surface roughness. Using the Minitab 2019 application, the obtained data was then analyzed to determine the influence of each trimming parameter working individually or simultaneously. Using the method of geometric factorial analysis, integrate the research parameters. The results indicated that the surface roughness increased as the feed rate increased; the lowest surface roughness was achieved with a depth of cut of 1 mm, a feeding rate of 47.5 mm/min, and a cut angle of 80°. In this study, the surface roughness value decreased as the cutting angle increased at lower levels of feeding rate, while the surface roughness value increased at higher levels of feeding rate.

Experimental Analysis of Surface Roughness Optimization of EN19 Alloy Steel Milling by the Cuckoo Search Algorithm

In the present paper, end milling has been performed on EN19 alloy steel by selecting cutting speed, feed rate, & depth of cut as input parameters and surface roughness (SR) as a response. EN19 alloy steel milling is widely used in various sectors, such as the automotive, defence, construction, aerospace, and nuclear sectors. A parametric study of EN19 alloy steel is needed for better machining. The central composite design was used for designing the experiments & modeling the surface roughness as a response. A cuckoo search algorithm was applied to minimize the surface roughness. It was found that feed rate is the most important factor affecting surface roughness (SR). The Cuckoo Search Algorithm also reveals that a minimum SR 1.8576 micrometer has been achieved at a higher speed of 765 RPM, a lower feed rate of 55.9516 mm/min., & a lower depth of cut of 0.4846 mm. The experiment concludes that it is so that the optimum SR is exhibited at both lower feed rates & high speeds. This, in turn, indicates that our implementation of CCD-based SR, followed by the real cuckoo search algorithm optimization, provides similar results and a good model to the practical results we would expect.

Analysis of Tool Load Concerning the Cross-Sectional Size of Removed Material

High-feed milling (HFM) represents a progressive manufacturing technology that has recently found widespread application across various industries. HFM is characterized by high machining speed, reduced cycle times, increased overall productivity, and increased tool life. Due to its versatility, HFM is a suitable technology for the application of various materials. The study deals with experimental analysis of cutting forces, machined surface integrity, and statistical evaluation in high-feed machining. In the present study, nickel-copper-based alloy (Monel) was chosen as the machined material, employing HFM with a monolithic ceramic milling cutter. The Monel material is characterized by its excellent mechanical properties and chemical resistance in harsh environments. During machining, cutting forces were recorded in three mutually perpendicular directions. This paper delves into the analysis of the impact of the depth of cut (ap), width of cut (ae), and lead-in angle (ε). The chosen evaluation characteristics encompass the tool load, primary profile, and the attained roughness of the machined surface. It is noteworthy that the technology under consideration predominantly aligns with the roughing phase of the manufacturing process. Additionally, the investigation incorporates a statistical analysis of the response surface pertaining to the cutting force components, namely Fx, Fy, Fz, and the resultant cutting force F.

Finite element simulation and experimental investigation of in-situ laser-assisted diamond turning of monocrystalline silicon

While the effectiveness of in-situ laser-assisted diamond turning (In-LAT) for promoting the ductile machinability of monocrystalline silicon has been demonstrated, the underlying cutting mechanisms remain inadequately understood. In this study, we investigate the fundamental mechanisms involved in the In-LAT of monocrystalline silicon by finite element (FE) simulations and experiments. Specifically, a FE model of In-LAT of monocrystalline silicon is developed, which incorporates a Drucker–Prager constitutive model to address the brittle fracture of the material, as well as temperature-dependent materials properties to address the thermal softening effect. Furthermore, experiments of In-LAT of monocrystalline silicon are conducted with the self-developed In-LAT device, including tapering cutting and end face cutting. Simulation results demonstrate that In-LAT significantly increases the critical depth of cut for the brittle-to-ductile transition of monocrystalline silicon in tapering cutting mode by 72.2% compared to conventional cutting, accompanied with significantly reduced cutting forces, continuous chip profile and reduced surface brittle damage. The promotion of ductile machinability of monocrystalline silicon under In-LAT is attributed to the reduction and dispersion of stress in the cutting zone, which is in contrast to the significant stress concentration at the rake face and cutting edge in conventional cutting. And simulation results also provide an optimal temperature field of 900 K for the In-LAT of monocrystalline silicon, above which the excessive plastic flow accompanied by thermal accumulation results into deteriorated surface roughness. These findings provide valuable insights for understanding the cutting mechanisms of In-LAT and the parameter optimization for In-LAT application.

The mechanism on surface integrity and fatigue performance of double‐sided micro‐cutting enhanced FeCoCrNiAl0.6 high entropy alloy

AbstractThis study examines the impact of cutting parameters on the fatigue properties of FeCoCrNiAl0.6 high‐entropy alloy under bi‐directional micro‐cutting. Utilizing Abaqus/fe−safe for analysis, we evaluated surface roughness, internal stress distribution, and fatigue longevity. Enhanced cutting speed resulted in smoother surfaces, whereas increased cut depth intensified surface roughness. Optimal cutting at 60,000 mm/min and 40 μm depth yielded a notable 91% rise in compressive stress (207 MPa) compared to slower speeds (108 MPa at 30,000 mm/min). Yield strength remained stable at 700 MPa across conditions. Fatigue resistance improved post‐bi‐directional cutting, with a 21.8–26.6% boost in life cycles, highlighting a consistent 10% advantage over single‐direction cutting.

Experimental study and multi-objective optimisation of CNC turning parameters of AL6061 materials

ABSTRACT This paper investigates the effectiveness of feed rate, depth of cut, and cutting speed on material removal rate (MRR) and surface roughness (SR) of CNC lathe machines on AL6061 Materials. A combined use of Taguchi, Response Surface Methodology (RSM), and Genetic Algorithms (GA) is proposed to study and optimise turning processes. Firstly, the RSM with a fractional factorial design is implemented to indicate the significance and influence of process parameters on RSM and MRR. With the achieved data, regression models are formulated to present the relationship between control factors and the responses and are proven reliable in predicting through statistical analysis and validating experiment results. Further, a multi-objective genetic algorithm (GA) was used to obtain a Pareto solution set with multiple combinations of factors for optimal MRR and SR. Then, the best trade-off between process parameters is analysed to achieve desired quality outcomes. The results indicate that the proposed approach provides an effective solution for CNC Turning machines and can extend to similar problems in various fields.

Effect of Some Cutting Parameters on Surface Finish of Bright Mild Steel

The objective of this paper was to evaluate the effect of some cutting parameters on surface finish of bright mild steel (BMS). Samples were cut into the required piece with dimension 50mm x 200mm and the turning operation done using a tungsten carbide tip tool. The cutting variables include speed of the spindle, feed rate and depth of cut. The result obtained shows that a feed rate of 0.75mm/rev affects the surface finish the most with a roughness value of 3µm, while the combination of a depth of cut of 0.2mm and maximum speed 700rpm have the minimum effect on the surface finish (Ra value of 1.1µm) of low carbon steel.

Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning

This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis, and rotary combined with dual-axis). The results suggest that the rotary (z-axis oscillation) ultrasonic-assisted method may provide better performance. Subsequently, this superior ultrasonic-assisted method was applied both with and without laser locally preheating assistance, respectively. Using a Taguchi orthogonal array, milling process parameters (spindle speed, feed rate, and radial depth of cut) were planned for experiments with the same cutting tool and the workpiece just mentioned above. The surface roughness serves as the objective function while being constrained by cutting-tool life. The characteristics of the smaller-the-better in the Taguchi method were applied to determine the optimal combination of process parameters. Based on the optimal milling process parameters obtained and the superior hybrid-assisted method adopted, milling experiments were repeatedly performed to collect the data on cutting force and cutting-tool wear. Feature engineering was performed on the cutting force signals, and different domain characteristics from both the time and frequency domains were extracted. Hereafter, feature selection by random forest and data standardization were further applied to feature extractions, and the data processing was thus completed. For the processed data, a cutting-tool wear prediction model was constructed by ensemble learning. This method leverages various machine learning regression models, including decision tree, random forest, extremely randomized tree, light gradient boosting machine, extreme gradient boosting, AdaBoost, stochastic gradient descent, support vector regression, linear support vector regression, and multilayer perceptron. After hyper-parameter tuning, the ensemble voting regression prediction was performed based on these ten mentioned models. The experimental results demonstrate that the ensemble voting regression model surpasses the performance of each individual machine learning regression model. In addition, this regression model achieves a coefficient of determination (R2) of 0.94576, a root mean square error (RMSE) of 0.24348, a mean squared error (MSE) of 0.05928, and a mean absolute error (MAE) of 0.18182. Therefore, the ensemble learning approach has been proven to be a feasible and effective method for monitoring cutting-tool wear.

Optimization of Machining Parameters on The Geometry Precision of Cortical Screw of Ti-6Al-4V ELI Using Gray Relational Analysis Method

Titanium and its alloys have been widely developed in aerospace, biomedical, electronics, sports, and offshore. In the biomedical field, titanium alloy that has been widely used, based on its properties, is biocompatible for bone implants and not becoming a foreign thing in the body. This study aims to optimize to determine the optimal conditions for cutting parameters for the precision of the cortical thread screw geometry referring to the ISO 5835 standard. In this study, the cutting was carried out using a CNC lathe, where the machining parameters used were spindle rotation of 100, 200, and 300 rpm, depth of cut of 0.01, 0.02, and 0.03 mm, and types of lubricants in the form of synthetic oil, virgin palm oil, and virgin coconut oil. Data processing in this study is analyzed using the Gray Relational Analysis Method to get an optimal combination of cutting conditions. The significant factor that influences the precision of cortical thread is the depth of cut, which is a contribution of 98%. It can illustrate that the smaller depth of cut will produce better screw geometry. The optimum conditions for cutting parameters that deliver the best level of precision were obtained at a spindle speed of 100 rpm, depth of cut of 0.01 mm, and type of lubricant of coconut oil. Meanwhile, the screw surface damage is stacked chips as a result of the high temperature generated during the cutting process.

Multiparameter signal-to-noise ratio optimization for end milling cutting conditions of aluminium alloy 5083

Surface integrity problems during selective material removal processes are a very common limitation for process productivity and part quality, especially in difficult-to-machine materials like 5083 aluminium alloy (AA), which is known for its remarkable performance in extreme environments. In general, tuning the cutting-part material properties with cutter geometry and cutting parameters can optimize surface texture, increase parts accuracy and resistance in corrosion, and eliminate process noise and energy waste. This work is an experimental study of surface parameter optimization during finish end milling of an AA5083 under a specific range of three cutting parameters with an optimized two-flute carbide cutter by previous work. So, twenty-seven experiments were run having varied the radial depth of cut (RDOC), feed rate (f), and cutting speed (S). Surface roughness parameters (Ra and Rt) were measured in the direction of cutting speed at three different distances by the upper edge. The signal-to-noise (SN) ratios have been calculated, and the process was optimized following the analysis of means. Then, additive models with linear interactions were fitted on SN ratios, and the analysis of variances and residual normality plots were utilized to validate the models’ goodness. The SN approach and analysis of means conclude that 0.5 mm RDOC, 6000 rpm speed, and 0.082 mm/tooth feed optimize the process and can effectively predict the Ra and Rt responses. The newly produced machinability data can benefit further applications of AA5083 in industrial applications such as shipbuilding and vehicle bodies.

High-performance grinding of ceramic matrix composites

Ceramic matrix composites (CMCs) are highly promising materials for the next generation of aero-engines. However, machining of CMCs suffers from low efficiency and poor surface finish, which presents an obstacle to their wider application. To overcome these problems, this study investigates high-efficiency deep grinding of CMCs, focusing on the effects of grinding depth. The results show that both the surface roughness and the depth of subsurface damage (SSD) are insensitive to grinding depth. The material removal rate can be increased sixfold by increasing the grinding depth, while the surface roughness and SSD depth increase by only about 10%. Moreover, it is found that the behavior of material removal is strongly dependent on grinding depth. As the grinding depth is increased, fibers are removed in smaller sizes, with the fiber length in chips being reduced by about 34%. However, too large a grinding depth will result in blockage by chip powder, which leads to a dramatic increase in the ratio of tangential to normal grinding forces. This study demonstrates that increasing the depth of cut is an effective approach to improve the machining efficiency of CMCs, while maintaining a good surface finish. It provides the basis for the further development of high-performance grinding methods for CMCs, which should facilitate their wider application.

Finite element and experimental analysis of surface integrity and surface roughness of precision machining SiCp/Al

The silicon carbide particle-reinforced matrix composites, or SiCp/Al, have excellent mechanical qualities, but producing them is quite difficult since the SiC particles’ and the Al matrix’s properties differ greatly from one another. During the cutting process, the Ra local oscillation phenomena takes place; that is, surface roughness seems to grow, decrease, and then increase as the cutting depth increases within a particular cutting depth range while maintaining the same cutting speed and feed. To find out why this behavior occurred, a cutting experiment and a simulation analysis of SiCp/Al composites were performed. Tests were conducted to find out how varied cutting depths affected surface roughness, and a finite element model for two-dimensional cutting was created. Based on the data, it can be concluded that Ra’s local oscillation phenomena is responsible for the surface quality at various cutting speeds. At 100 mm/s, 200 mm/s, 300 mm/s, and 400 mm/s, respectively, the mutation’s surface quality improved by 29.6%, 14.3%, 19.6%, and 30.7% prior to the cutting depth. The analysis is justified by the fact that the depth of cut is increasing, the way in which particles are removed has changed and the percentage of scratches appears to be decreasing. As aluminium is a plastic material, plastic deformation occurs during cutting by the sub-cutting edge of the extrusion, coating the machined surfaces and producing fine cracks rather than plough furrows. In a sense, increasing the cutting thickness results in larger chips, more force on the particles in the cutting path and easier removal. At the same time, it reduces the width of the chip in future cuts, improving the surface quality of the process.